PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES

Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations. The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation. The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users. The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems

A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System

Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets

A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System

Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets

A Machine Learning-Based Course Enrollment Recommender System

At Northeastern Illinois University, the Computer Science (CS) department offers students a wide range of unique courses. These courses reflect the diversity of options available at the university. Students can choose from three concentrations within the CS major. Nevertheless, students pursuing a degree in computer science will not always be enrolled in the same courses. They often have difficulties choosing which courses to enroll in as part of their concentration. This research proposes a personalized course recommender system to help current and future students find the courses they need to enroll in. It will generate a list of suggested courses for each student to consider registering for the upcoming semester. The system utilizes a collaborative filtering algorithm to profile each student with respect to their registration preferences. The algorithm extracts latent features of students and courses by performing stochastic gradient descent to optimize our objective function. The learning process takes into account auxiliary information, such as course instructors, meeting times, and delivery methods (online/in-person/hybrid). When making recommendations, the filtering step of the system follows program requirements to refine the output by removing unnecessary courses and substituting courses with their prerequisites when necessary. We evaluated our proposed model on a CS enrollment dataset. In the experiments, we conducted an extensive study on the hyperparameters of the model and visualized each parameter. The results show that our model can produce high-quality recommendations, whose accuracy is comparable to state-of-the-art research. A web application that demonstrates the framework is also implemented

A group preference-based privacy-preserving POI recommender system

Ubiquitous smartphones with their built-in location services enable people to explore various points-of-interest (POIs) through location-based apps, e.g., Yelp and Foursquare City Guide. With these apps, users can receive personalized recommendations on nearby places, e.g., restaurants and arcades, which not only saves them searching time, but also helps find POIs that are of interest to them. One issue with these apps and almost all existing recommender systems is that they require users to share their preference data with the service providers. This information, if not properly used, can leak users’ privacy. In this paper, we propose a group preference-based POI recommendation scheme which fuses matrix factorization and clustering techniques to provide quality recommendations without sacrificing users’ privacy

Improving the Accuracy for Privacy-Preserving Point-of-Interest Recommender Systems

In the digital era, an essential ingredient of numerous online vendors and various types of websites is a recommender system. This technology immensely reduces users’ search time when looking for the contents of their interest. Recommender systems make suggestions based on individual preferences learned from their users. Generally speaking, users have to unconditionally share personal search and/or purchase history with service providers, who also have full access to their private preferences. In this research, we have implemented a privacy-preserving point-of-interest recommender system based on a framework with three major components: a mobile app IncogniToGo, a vendor-hosted aggregate server, and a remote central server. Furthermore, we improved the system’s prediction accuracy by estimating each anonymous user’s GPS location and incorporating this information into the recommendation process. Our current model integrates the Google Cloud Platform (Maps and Firebase), a wireless communication standard called Wi-Fi Direct, and machine learning algorithms. IncogniToGo allows a user to rate a place in Google Maps. Internally, it computes the user’s current location based on their rated places and communicates this data using a random user ID via Wi-Fi Direct to the aggregator server. User groups are created on the aggregator server, and the corresponding group preferences are then sent to the Firebase (central server). Machine learning algorithms are performed on the server to extract latent features of the shared data. Finally, IncogniToGo pulls such features from Firebase and generates personalized recommendations locally on the user’s device, which prevents the server from learning users’ individual preferences